Molecular model established by detecting serum specimen based on proteomics and used for assisting in evaluating renal injury progress of diabetic nephropathy as well as construction method and application of molecular model

Abstract

The invention relates to a molecular model established based on proteomics detection serum specimens and used for assisting in evaluating the renal injury progress of diabetic nephropathy as well as a construction method and application of the molecular model, new disease diagnosis markers can be found, and an existing disease diagnosis system can be optimized; according to the technical scheme, a proteomics technology is combined with various medical diagnosis technologies such as experiments and images through research thinking logic of evidence-based medicine and clinical epidemiology so as to diagnose whether a patient suffers from diseases or not and identify different diseases, and the molecular model is formed by combining proteins P23396 + P0CG47 + Q9NYQ8 + P63241, according to the present invention, the protein composition in the sample is detected by using the high-throughput mass spectrometry means, the difference of the diabetic nephropathy disease group and the non-disease group on the proteomics level is compared, the biomarker for the disease progress auxiliary evaluation model construction is screened, and the new scheme is provided for the disease progress auxiliary evaluation of the diabetic nephropathy.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a molecular model for assisting in the assessment of the progression of kidney damage in diabetic nephropathy based on proteomics detection of serum samples, and a construction method and application thereof. Background Art

[0002] Diabetic nephropathy (DN) is one of the most common and serious microvascular complications of diabetes and has become the leading cause of chronic kidney disease in my country. Due to the complex pathogenesis of DN, the lack of specific intervention targets, and extremely limited treatment options, the incidence of chronic kidney disease continues to rise.

[0003] Clinical proteomics is a discipline that applies proteomics technology to clinical diagnosis, treatment, prognosis and etiology research. By combining proteomics technology with clinical epidemiology, medical statistics, bioinformatics, artificial intelligence and molecular biology, the effects and mechanisms of protein molecules related to diseases are explored to prevent the occurrence of diseases, improve the level of diagnosis and treatment, accelerate the recovery of diseases and promote human health.

[0004] Proteomic diagnostic research provides a basis for disease diagnosis; based on proteomic technology and research strategies, new disease diagnostic markers are discovered at the protein expression level, or the existing disease diagnostic system is optimized; through the research thinking logic of evidence-based medicine and clinical epidemiology, proteomic technology is combined with various medical diagnostic technologies such as experiments and imaging to diagnose whether the disease is serious or not and to differentiate between different diseases, and compare with the "gold standard" to evaluate the authenticity, reliability and practicality of diagnostic tests. Therefore, it is imperative to invent a diagnostic molecular model based on proteomic detection of serum samples of patients with diabetic nephropathy to evaluate disease progression. Summary of the invention

[0005] In view of the above situation, in order to solve the defects of the prior art, the purpose of the present invention is to provide a molecular model based on proteomics detection of serum samples to assist in the assessment of the progression of kidney damage in diabetic nephropathy, as well as its construction method and application, which can discover new disease diagnostic markers and optimize the existing diagnostic system of the disease; through the research thinking logic of evidence-based medicine and clinical epidemiology, proteomics technology is combined with various medical diagnostic technologies such as experiments and imaging to diagnose whether the disease is present and distinguish between different diseases.

[0006] The technical solution provided by the present invention is that the molecular model is composed of a combination of proteins P23396 + P0CG47 + Q9NYQ8 + P63241.

[0007] The method for constructing the molecular model comprises the following steps: 1) Preparation of protein-enriched nanomagnetic bead samples Take a diabetic serum sample, dilute it with a loading buffer, mix it evenly with the nanomagnetic bead suspension, incubate it at 37°C for 1 hour, wash the nanomagnetic beads twice with the loading buffer, and then wash the nanomagnetic beads once with a loading buffer without CHAPS, adsorb the nanomagnetic beads on a magnetic stand, discard the supernatant, and obtain a protein-enriched nanomagnetic bead sample; 2) Preparation of peptide samples Add lysis buffer to the protein-enriched nanomagnetic bead sample prepared in step 1), incubate at 60°C for 30 min for reductive alkylation reaction, add an equal volume of ddH2O, dilute the SDC concentration to less than 0.5%, add trypsin, incubate at 37°C with shaking overnight for enzyme digestion, add TFA to terminate the enzyme digestion the next day, take the supernatant for desalting with SDB-RPS desalting column, vacuum dry and freeze at -20°C to obtain peptide samples for later use; 3) Mass spectrometry detection of peptide samples The samples were detected by mass spectrometry using an UltiMate 3000 RSLCnano nanoliter liquid phase tandem timsTOF Pro mass spectrometer. The peptide samples prepared in step 2) were injected through an automatic sampler, bound to a C18 trapping column, and then separated in an analytical column. Mobile phase A and mobile phase B were used to establish an analytical gradient, and mass spectrometry was used for data acquisition in diaPASEF mode. According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software, and the collision energy was set to increase linearly from 59 eV at 1 / K0=1.6 Vs / cm^2 to 20 eV at 1 / K0=0.6 Vs / cm^2 according to the ion mobility, and DIA raw data were obtained. 4) Analyze DIA raw data; The DIA raw data obtained in step 3) were analyzed using the library-free mode of the DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database (20220209) downloaded from Uniprot. The search parameters were set by default. The protein quantitative information was normalized using the MaxLFQ algorithm to obtain proteomics data. 5) Perform differential protein analysis and functional enrichment analysis on proteomics data The proteomics data obtained in step 4) were processed. First, contaminating proteins were removed and logarithmic transformation was performed. Then, the missing values ​​were estimated using values ​​representing the normal distribution around the detection limit of the mass spectrometer. For continuous variables that conformed to the normal distribution, the mean and standard deviation were used to describe them. For continuous variables that did not conform to the normal distribution, the quartiles were used to describe them. For categorical variables, the percentage was used to describe them. For the test between two groups of data, the t test was used for data that conformed to the normal distribution, and the rank sum test was used for data that did not conform to the normal distribution. For the screening of biomarkers, the difference fold (FC) and statistical test (p ≤ 0.05) were selected as the basis for preliminary screening. LASSO regression and machine learning method extreme gradient boosting (XGBoost) were used for further screening of biomarkers, and finally protein markers were screened out. 5) Use the final screened protein markers to build a diagnostic model The protein markers were screened using WPCNA (Weighted Protein Co-Expression Network Analysis), and the different modules generated after clustering were associated with the groups to find the module most relevant to the grouping. All the proteins in the module were annotated and enriched, and then the top 20 proteins in the module were further selected through random forest importance sorting. Multiple ordered logistic regression models were constructed by random combination to predict the grouping variables. After five-fold cross-validation, the prediction model with the largest average AUC was found as the optimal prediction model.

[0008] In the step 1), the composition of the loading buffer is: 10mM Tris-Cl, 1mM EDTA, 150mM KCl, 0.05% CHAPS; the composition of the loading buffer without CHAPS is: 10mM Tris-Cl, 1mM EDTA, 150mM KCl.

[0009] In the step 2), the lysis solution composition is: 1% SDC / 100 mM Tris-HCl, pH=8.5 / 10 mM TCEP / 40 mM CAA.

[0010] In the step 3), the parameters of the trapping column are: 75 µm*2 cm, 3 µm particle size, 100 Å pore size, Thermo, the parameters of the analytical column are: 75 µm*25 cm, 1.6 µm particle size, 100 Å poresize, IonOpticks, the mobile phase A is 0.1% formic acid, the mobile phase B is 0.1% formic acid in ACN, and the flow rate of the analysis is set to 300 nL / min.

[0011] In the step 3), the capillary voltage was set to 1400 V, the scanning range of the MS1 and MS2 spectra was set to 100-1700 m / z, the ion mobility range was set to 0.6-1.6 Vs / cm2, and the accumulation time and ramp time were set to 100 ms.

[0012] In step 4), the search parameters are as follows: enable the Precursor ion generation option to predict the theoretical spectral library; use Trypsin / P, and allow up to 2 missed cleavage sites; Carbamidomethyl (C) modification is set as a fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) are set as variable modifications; MS1 and MS2 mass tolerances are set to 15 ppm; MBR is enabled; Heuristic protein inference is enabled; and FDR is set to 1%.

[0013] In step 5), the proteomics data are processed as follows: the mean and standard deviation of the actual quantitative values ​​are calculated, a new data set is created, whose mean is the original mean minus 1.8 original standard deviations and whose standard deviation is 0.25 original standard deviations, and the new data set is used to fill in missing values. Finally, proteins with missing ratios exceeding 50% in both groups are removed, and the remaining proteins are selected for subsequent analysis.

[0014] The invention relates to the use of a molecular model for assisting in the assessment of diabetic nephropathy kidney damage established based on proteomics detection of serum samples in the preparation of a kit for assisting in the assessment of diabetic nephropathy kidney damage.

[0015] The present invention detects the protein composition in the sample by means of high-throughput mass spectrometry, compares the differences between the diabetic nephropathy group and the non-diseased group at the proteomic level, screens out biomarkers for the construction of a disease progression auxiliary assessment model, and combines important clinical variables to construct a clinical disease progression auxiliary assessment molecular model, and evaluates the model. The results show that a disease progression auxiliary assessment molecular model can be established based on proteomic detection of serum specimens of diabetic nephropathy patients, which provides a new solution for the auxiliary assessment of disease progression of diabetic nephropathy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The main process is constructed for the model of the present invention.

[0017] Figure 2 The protein module screened by WPCNA (Weighted Protein Co-Expression Network Analysis) of the present invention has 400 proteins that have the most obvious correlation with the patient's glomerular filtration rate, urine total protein, and urine albumin.

[0018] FIG3 shows the top 20 proteins in the WPCNA module screened by random forest importance ranking in the present invention.

[0019] FIG4 shows that the present invention combines <= 5 proteins into a model, and finally forms 2000 models, which are obtained after five-fold cross validation.

[0020] Figure 5 For the present invention, the model with the largest mAUC was selected as the final model, namely label ~ P23396 + P0CG47 + Q9NYQ8 + P63241, which included 4 proteins.

[0021] Figure 6 For the evaluation of the model of the present invention, the prediction accuracy of the model in the training set is 0.803, and the prediction accuracy in the test set is 0.722. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention is further described in detail below with reference to the accompanying drawings and examples. Example

[0023] The present invention, when implemented, specifically comprises the following steps: 1) Preparation of protein-enriched nanomagnetic bead samples Serum samples of diabetes ACR < 30, mild diabetes and renal damage 300 > ACR > 30, and severe diabetes and renal damage ACR > 300 were collected respectively; 20 μl of sample was taken, diluted with loading buffer (10mM Tris-Cl, 1mM EDTA, 150mM KCl, 0.05% CHAPS), mixed evenly with 1mg superparamagnetic iron oxide nanoparticle suspension, incubated at 37°C for 1 hour, washed superparamagnetic iron oxide nanoparticles twice with loading buffer, and then washed once with loading buffer without CHAPS (10mMTris-Cl, 1mM EDTA, 150mM KCl), adsorbed superparamagnetic iron oxide nanoparticles on a magnetic stand, and the supernatant was discarded to obtain a protein-enriched nanoparticle sample; 2) Preparation of peptide samples Add lysis buffer (1% SDC / 100 mM Tris-HCl, pH=8.5 / 10 mM TCEP / 40 mM CAA) to the protein-enriched nanomagnetic bead sample prepared in step 1), incubate at 60°C for 30 min for reductive alkylation reaction, add an equal volume of ddH2O to dilute the SDC concentration to less than 0.5%, add 1 μg trypsin, incubate at 37°C with shaking overnight for enzyme digestion, add TFA to terminate enzyme digestion the next day, take the supernatant for desalting with SDB-RPS desalting column, vacuum dry and freeze at -20°C to obtain peptide samples for later use; 3) Mass spectrometry detection of peptide samples The samples were detected by mass spectrometry using an UltiMate 3000 RSLCnano nanoliter liquid phase (Thermo) coupled to a timsTOF Pro mass spectrometer (Bruker). The peptide samples prepared in step 2) were injected through an autosampler and bound to a C18 trapping column (75 µm*2 cm, 3 µm particle size, 100 Å pore size, Thermo). They then entered the analytical column (75 µm*25 cm, 1.6 µm particle size, 100 Å pore size, IonOpticks) for separation. The analytical gradient was established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The flow rate of the analysis was set to 300 nL / min, and the mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400 V, the scan range of MS1 and MS2 spectra was set to 100–1700 m / z, the ion mobility range was set to 0.6–1.6 Vs / cm2, and the accumulation time and ramp time were set to 100 ms.

[0024] According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software. The collision energy was set to increase linearly from 59 eV (1 / K0=1.6 Vs / cm^2) to 20 eV (1 / K0=0.6 Vs / cm^2) according to the ion mobility, and the DIA raw data was obtained. 4) Analyze DIA raw data; The DIA raw data obtained in step 3) were analyzed using the library-free mode of the DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database (20220209) downloaded from Uniprot. The search parameters were set by default. The key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and a maximum of 2 missed cleavage sites were allowed; Carbamidomethyl (C) modification was set as a fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15 ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%, and protein quantitative information was normalized using the MaxLFQ algorithm to obtain proteomics data; 5) Perform differential protein analysis and functional enrichment analysis on proteomics data For the proteomics data processing obtained in step 4), first, contaminating proteins were removed and logarithmic transformation was performed. Then, missing values ​​were estimated using values ​​representing the normal distribution around the detection limit of the mass spectrometer. In short, the mean and standard deviation of the actual quantitative values ​​were calculated, and a new data set was created whose mean was the original mean minus 1.8 original standard deviations and whose standard deviation was 0.25 original standard deviations. The new data set was used to fill in missing values. Finally, proteins with missing ratios of more than 50% in both groups were removed, and the remaining proteins were selected for subsequent analysis.

[0025] Descriptive statistical analysis: for continuous variables that conform to normal distribution, mean and standard deviation are used for description; for continuous variables that do not conform to normal distribution, quartiles are used for description; and categorical variables are described by percentage. For the test between two groups of data, t-test is used for data that conform to normal distribution, and rank sum test is used for data that do not conform to normal distribution. For the screening of biomarkers, the difference multiple (FC) and statistical test (p≤0.05) are selected as the basis for initial screening, and LASSO regression and machine learning method extreme gradient boosting (XGBoost) are used for further screening of biomarkers, and finally protein markers are screened out; 6) Use the final screened protein markers to build a diagnostic model The protein markers screened in step 5) were screened using WPCNA (Weighted Protein Co-Expression Network Analysis). This analysis method aims to find co-expressed protein modules and explore the association between the protein network and the phenotype of interest, as well as the core proteins in the network. The different modules generated after clustering were analyzed for association with the groups to find the modules most relevant to the groups (such as Figure 1 The blue module was annotated and enriched for all proteins in the module. The top 20 proteins in the module were further sorted by random forest importance. Multiple ordered logistic regression models were constructed by random combination to predict the grouping variables. After five-fold cross-validation, the prediction model with the largest average AUC was found as the optimal prediction model.

[0026] The present invention detects the protein composition in the sample by means of high-throughput mass spectrometry, compares the proteomic differences between the diabetic nephropathy group and the non-disease group, screens out biomarkers for the construction of a molecular model for auxiliary evaluation of disease progression, and combines important clinical variables to construct a molecular model for auxiliary evaluation of disease progression, and evaluates the model, achieving satisfactory technical results. The relevant experimental data are as follows: 1. Sample information Patients with clinical diagnosis of diabetes, with or without abnormal urine protein, were divided into the following groups: diabetes (ACR < 30) vs mild diabetes with renal damage (300 > ACR > 30) vs severe diabetes with renal damage (ACR > 300).

[0027] 2. Experimental Methods 2.1 Sample processing The sample was enriched using superparamagnetic iron oxide nanoparticles. Take 20 μl of sample, dilute with loading buffer (10 mMTris-Cl, 1 mM EDTA, 150 mM KCl, 0.05% CHAPS), mix evenly with 1 mg nanoparticle suspension, and incubate at 37 °C for 1 hour. Wash the magnetic beads twice with loading buffer, and then wash the magnetic beads once with loading buffer without CHAPS (10 mM Tris-Cl, 1 mM EDTA, 150 mM KCl). Adsorb the magnetic beads on the magnetic stand, discard the supernatant, and obtain the protein-enriched nanoparticles. Add lysis buffer (1% SDC / 100 mM Tris-HCl, pH=8.5 / 10 mM TCEP / 40 mMCAA) to the sample and incubate at 60 °C for 30 min for reductive alkylation reaction. Add an equal volume of ddH2O to dilute the SDC concentration to less than 0.5%, add 1 μg of trypsin, and incubate at 37°C with shaking overnight for enzyme digestion. The next day, add TFA to terminate the enzyme digestion, take the supernatant and desalt it with an SDB-RPS desalting column, vacuum dry it, and freeze it at -20°C for later use.

[0028] 2.2 LC-MS / MS analysis The mass spectrometry of the samples was detected using an UltiMate 3000 RSLCnano nanoliter liquid phase (Thermo) coupled to a timsTOFPro mass spectrometer (Bruker). The peptide samples were injected by an autosampler, coupled to a C18 trapping column (75 µm*2 cm, 3 µmparticle size, 100 Å pore size, Thermo), and then separated in an analytical column (75 µm*25 cm, 1.6 µmparticle size, 100 Å pore size, IonOpticks). The analytical gradient was established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The flow rate of the analysis was set to 300 nL / min. The mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400 V. The scan range of the MS1 and MS2 spectra was set to 100-1700 m / z. The ion mobility range was set to 0.6-1.6 Vs / cm2. Accumulation time and ramp time were set to 100 ms.

[0029] According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software. The collision energy was set to increase linearly from 59 eV at 1 / K0=1.6 Vs / cm^2 to 20 eV at 1 / K0=0.6 Vs / cm^2 according to the ion mobility.

[0030] 2.3 Search Library DIA raw data were analyzed using the library-free mode of DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database (20220209) downloaded from Uniprot. The search parameters were mainly set by default, and the key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and up to 2 missed cleavage sites were allowed; Carbamidomethyl (C) modification was set as fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%. Protein quantitative information was normalized using the MaxLFQ algorithm.

[0031] 2.4 Data Processing For proteomics data processing, first, contaminating proteins were removed and logarithmic transformation was performed. After that, missing values ​​were estimated using values ​​representing a normal distribution around the detection limit of the mass spectrometer. In short, the mean and standard deviation of the actual quantitative values ​​were calculated, and a new data set was created with a mean of the original mean minus 1.8 original standard deviations and a standard deviation of 0.25 original standard deviations, and the missing values ​​were filled using the new data set. Finally, proteins with a missing ratio of more than 50% in both groups were removed, and the remaining proteins were selected for subsequent analysis.

[0032] 2.5 Statistical analysis Descriptive statistical analysis, for continuous variables that conform to normal distribution, use mean and standard deviation to describe, for continuous variables that do not conform to normal distribution, use quartiles to describe, and for categorical variables, use percentage to describe. For the test between two groups of data, the t test is used for data that conform to normal distribution, and the rank sum test is used for data that do not conform to normal distribution. For the screening of biomarkers, the difference multiple (FC) and statistical test (p≤0.05) are selected as the basis for preliminary screening, and LASSO regression and machine learning method extreme gradient boosting method (XGBoost) are used for further screening of biomarkers. Finally, the selected markers are modeled by selecting appropriate modeling methods according to actual conditions. For modeling data processing, the modeling data is planned to be split, 70% of the data is used as a training set, and 30% of the data is used as a test set. It is planned to evaluate the model from three aspects: evaluate the model from the discrimination through c-Index, ROC area AUC or c-statistics, evaluate the calibration and consistency of the model through calibration curves and other methods, and evaluate the clinical effect of the model through DCA clinical decision curve analysis or nomograms and other methods. Statistical analysis and model building methods were performed using R language or Python.

[0033] 3. Data Results 3.1 Main process of model building (such as Figure 1 ) 3.2 Experimental Results The mass spectrometry was screened using WPCNA (Weighted Protein Co-Expression Network Analysis), which aims to find co-expressed protein modules and explore the association between the protein network and the phenotype of interest, as well as the core proteins in the network. The different modules generated after clustering were analyzed for association with the groups to find the modules most relevant to the groups ( Figure 1 The blue module in the module was annotated and enriched. The top 20 proteins in the module were further selected by random forest importance ranking. Multiple ordered logistic regression models were constructed by random combination to predict the grouping variables. After five-fold cross-validation, the prediction model with the largest average AUC was found as the optimal prediction model.

[0034] The experimental results show that the protein module screened by WPCNA (Weighted Protein Co-Expression Network Analysis) of the present invention has 400 proteins that are most significantly correlated with the patient's glomerular filtration rate, urine total protein, and urine albumin, such as Figure 2 as shown

[0035] The present invention screens the top 20 proteins in the WPCNA module through random forest importance ranking, as Figure 3 shown

[0036] The present invention combines <=5 proteins into a model, and finally forms 2000 models. After five-fold cross-validation, the model as Figure 4 shown is obtained

[0037] The present invention selects the one with the largest mAUC as the final model, that is, label ~ P23396 + P0CG47 + Q9NYQ8 + P63241, which includes 4 proteins. As Figure 5 shown, the prediction probability calculation of a single sample is as follows prob1 = Φ(-77.35035 + 0.7944053 * quantitative value of P23396 protein + 3.0162091 * quantitative value of P0CG47 protein + 0.1237561 * quantitative value of Q9NYQ8 protein + 0.4714176 * quantitative value of P63241 protein) prob2 = Φ(-75.19791 + 0.7944053 * quantitative value of P23396 protein + 3.0162091 * quantitative value of P0CG47 protein + 0.1237561 * quantitative value of Q9NYQ8 protein + 0.4714176 * quantitative value of P63241 protein) - prob1 prob3 = 1 - prob1 - prob2 prob1 is the probability of predicting the first category (mild, ACR < 30) prob2 is the probability of predicting the second category (moderate, 30 <= ACR <= 300) prob3 is the probability of predicting the third category (severe, 300 < ACR) The prediction accuracy of the model of the present invention in the training set is 0.803, and the prediction accuracy in the test set is 0.722, as Figure 6 shown

[0038] 4. Conclusion The present invention first detects the protein composition in a sample by means of high-throughput mass spectrometry, compares the differences between the diabetic nephropathy patient group and the non-patient group at the proteomics level, screens out biomarkers for the construction of a molecular model for the auxiliary evaluation of disease progression, combines important clinical variables, conducts the construction of the auxiliary evaluation of clinical disease progression, and evaluates the model. The above results show that a molecular model for the auxiliary evaluation of disease progression is established based on the detection of serum specimens of diabetic nephropathy patients by proteomics, providing a new solution for the auxiliary evaluation of the disease progression of diabetic nephropathy

[0039] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A molecular model for assisting the assessment of kidney damage in diabetic nephropathy based on proteomics detection of serum samples, characterized in that: It is composed of the protein combination P23396 + P0CG47 + Q9NYQ8 + P63241.

2. The method for constructing a molecular model for assisting in the assessment of kidney damage in diabetic nephropathy based on proteomics detection of serum samples according to claim 1, characterized in that: The following steps are involved: 1) Preparation of protein-enriched nanomagnetic bead samples Take a diabetic serum sample, dilute it with a loading buffer, mix it evenly with the nanomagnetic bead suspension, incubate it at 37°C for 1 hour, wash the nanomagnetic beads twice with the loading buffer, and then wash the nanomagnetic beads once with a loading buffer without CHAPS, adsorb the nanomagnetic beads on a magnetic stand, discard the supernatant, and obtain a protein-enriched nanomagnetic bead sample; 2) Preparation of peptide samples Add lysis buffer to the protein-enriched nanomagnetic bead sample prepared in step 1), incubate at 60°C for 30 min for reductive alkylation reaction, add an equal volume of ddH2O, dilute the SDC concentration to less than 0.5%, add trypsin, incubate at 37°C with shaking overnight for enzyme digestion, add TFA to terminate the enzyme digestion the next day, take the supernatant for desalting with SDB-RPS desalting column, vacuum dry and freeze at -20°C to obtain peptide samples for later use; 3) Mass spectrometry detection of peptide samples The samples were detected by mass spectrometry using an UltiMate 3000 RSLCnano nanoliter liquid phase tandem timsTOF Pro mass spectrometer. The peptide samples prepared in step 2) were injected through an automatic sampler, bound to a C18 trapping column, and then separated in an analytical column. Mobile phase A and mobile phase B were used to establish an analytical gradient, and mass spectrometry was used for data acquisition in diaPASEF mode. According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software, and the collision energy was set to increase linearly from 59 eV at 1 / K0=1.6 Vs / cm^2 to 20 eV at 1 / K0=0.6 Vs / cm^2 according to the ion mobility, and DIA raw data were obtained. 4) Analyze DIA raw data; The DIA raw data obtained in step 3) were analyzed using the library-free mode of the DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database downloaded from Uniprot. The search parameters were set by default. The protein quantitative information was normalized using the MaxLFQ algorithm to obtain the proteomics data. 5) Perform differential protein analysis and functional enrichment analysis on proteomics data The proteomics data obtained in step 4) were processed. First, contaminating proteins were removed and logarithmic transformation was performed. Then, the missing values ​​were estimated using values ​​representing the normal distribution around the detection limit of the mass spectrometer. For continuous variables that conformed to the normal distribution, the mean and standard deviation were used to describe them. For continuous variables that did not conform to the normal distribution, the quartiles were used to describe them. Categorical variables were described using percentages. For the test between two groups of data, the t-test was used for data that conformed to the normal distribution, and the rank sum test was used for data that did not conform to the normal distribution. For the screening of biomarkers, the difference multiple FC and statistical test p≤0.05 were selected as the basis for preliminary screening. LASSO regression and the machine learning method extreme gradient boosting method were used for further screening of biomarkers, and finally protein markers were screened out; 5) Use the final screened protein markers to build a diagnostic model The protein markers were screened using WPCNA (Weighted Protein Co-Expression Network Analysis), and the different modules generated after clustering were associated with the groups to find the module most relevant to the grouping. All the proteins in the module were annotated and enriched, and then the top 20 proteins in the module were further selected through random forest importance sorting. Multiple ordered logistic regression models were constructed by random combination to predict the grouping variables. After five-fold cross-validation, the prediction model with the largest average AUC was found as the optimal prediction model.

3. The method for constructing an auxiliary diagnosis molecular model based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: In the step 1), the composition of the loading buffer is: 10mM Tris-Cl, 1mM EDTA, 150mM KCl, 0.05% CHAPS; the composition of the loading buffer without CHAPS is: 10mM Tris-Cl, 1mM EDTA, 150mM KCl.

4. The method for constructing a molecular model for auxiliary diagnosis based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: In the step 2), the lysis solution composition is: 1% SDC / 100 mM Tris-HCl, pH=8.5 / 10 mM TCEP / 40 mM CAA.

5. The method for constructing an auxiliary diagnosis molecular model based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: In step 3), the parameters of the trapping column are: 75 µm*2 cm, 3 µmparticle size, 100 Å pore size, Thermo, the parameters of the analytical column are: 75 µm*25 cm, 1.6 µmparticle size, 100 Å pore size, IonOpticks, the mobile phase A is 0.1% formic acid, the mobile phase B is 0.1% formic acid in ACN, and the flow rate of the analysis is set to 300 nL / min; In step 3), the capillary voltage was set to 1400 V, the scanning range of MS1 and MS2 spectra was set to 100-1700 m / z, and the ion mobility range was set to 0.6-1.6 Vs / cm 2 , Accumulation time and ramp time are set to 100 ms.

6. The method for constructing a molecular model for auxiliary diagnosis based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: In step 4), the search parameters are as follows: enable the Precursor ion generation option to predict the theoretical spectral library; use Trypsin / P, and allow up to 2 missed cleavage sites; Carbamidomethyl (C) modification is set as fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) are set as variable modifications; MS1 and MS2 mass tolerances are set to 15 ppm; MBR is enabled; Heuristic protein inference is enabled; and FDR is set to 1%.

7. The method for constructing a molecular model for auxiliary diagnosis based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: In step 5), the proteomics data are processed as follows: the mean and standard deviation of the actual quantitative values ​​are calculated, a new data set is created, whose mean is the original mean minus 1.8 original standard deviations and whose standard deviation is 0.25 original standard deviations, and the new data set is used to fill in missing values. Finally, proteins with missing ratios exceeding 50% in both groups are removed, and the remaining proteins are selected for subsequent analysis.

8. The method for constructing an auxiliary diagnosis molecular model based on proteomics detection of diabetic nephropathy serum specimens according to claim 2, characterized in that: The following steps are involved: 1) Preparation of protein-enriched nanomagnetic bead samples Serum samples of diabetes ACR < 30, mild diabetes and renal damage 300 > ACR > 30, and severe diabetes and renal damage ACR > 300 were collected respectively; 20 μl of sample was taken, diluted with a loading buffer composed of 10mM Tris-Cl, 1mM EDTA, 150mM KCl, and 0.05% CHAPS, and then mixed evenly with 1mg superparamagnetic iron oxide nanoparticle suspension, incubated at 37°C for 1 hour, and the superparamagnetic iron oxide nanoparticles were washed twice with the loading buffer, and then washed once with a loading buffer composed of 10mM Tris-Cl, 1mMEDTA, 150mM KCl without CHAPS, and the superparamagnetic iron oxide nanoparticles were adsorbed on a magnetic stand, and the supernatant was discarded to obtain a protein-enriched nanoparticle sample; 2) Preparation of peptide samples Add 1% SDC / 100 mM Tris-HCl, pH=8.5 / 10mM TCEP / 40 mM CAA lysis buffer to the protein-enriched nanomagnetic bead sample prepared in step 1), incubate at 60°C for 30 min for reductive alkylation reaction, add an equal volume of ddH2O to dilute the SDC concentration to less than 0.5%, add 1 μg trypsin, incubate at 37°C with shaking overnight for enzyme digestion, add TFA to terminate enzyme digestion the next day, take the supernatant for desalting with SDB-RPS desalting column, vacuum dry and freeze at -20°C to obtain peptide samples for later use; 3) Mass spectrometry detection of peptide samples The samples were detected by mass spectrometry using an UltiMate 3000 RSLCnano nanoliter liquid phase tandem timsTOF Pro mass spectrometer. The peptide samples prepared in step 2) were injected through an automatic sampler and bound to a C18 trapping column with the following parameters: 75 µm*2 cm, 3 µm particle size, 100 Å pore size, Thermo. They then entered the analytical column with the following parameters: 75 µm*25 cm, 1.6 µm particle size, 100 Å pore size, IonOpticks for separation. The analytical gradient was established using mobile phase A with 0.1% formic acid and mobile phase B with 0.1% formic acid in ACN. The flow rate of the analysis was set to 300 nL / min, and the mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400 V, the scan range of MS1 and MS2 spectra was set to 100–1700 m / z, the ion mobility range was set to 0.6–1.6 Vs / cm2, and the accumulation time and ramp time were set to 100 ms; According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software. The collision energy was set to increase linearly from 59 eV at 1 / K0=1.6 Vs / cm^2 to 20 eV at 1 / K0=0.6 Vs / cm^2 according to the ion mobility, and the DIA raw data was obtained. 4) Analyze DIA raw data; The DIA raw data obtained in step 3) were analyzed using the library-free mode of the DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database downloaded from Uniprot. The search parameters were set by default. The key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and a maximum of 2 missed cleavage sites were allowed; Carbamidomethyl (C) modification was set as a fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15 ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%, and protein quantitative information was normalized using the MaxLFQ algorithm to obtain proteomics data; 5) Perform differential protein analysis and functional enrichment analysis on proteomics data For the proteomics data obtained in step 4), first, contaminating proteins were removed and logarithmic transformation was performed. Then, the missing values ​​were estimated using values ​​representing the normal distribution around the detection limit of the mass spectrometer. Specifically, the mean and standard deviation of the actual quantitative values ​​were calculated, and a new data set was created with a mean of the original mean minus 1.8 original standard deviations and a standard deviation of 0.25 original standard deviations. The missing values ​​were filled using the new data set. Finally, proteins with missing ratios exceeding 50% in both groups were removed, and the remaining proteins were selected for subsequent analysis. For continuous variables that conformed to the normal distribution, the mean and standard deviation were used to describe them. For continuous variables that did not conform to the normal distribution, quartiles were used to describe them, and percentages were used to describe categorical variables. For the test between the two groups of data, the t test was used for those that conformed to the normal distribution, and the rank sum test was used for those that did not conform to the normal distribution. For the screening of biomarkers, the difference multiple and statistical test p≤0.05 were selected as the basis for preliminary screening, and LASSO regression and machine learning method extreme gradient boosting were used for further screening of biomarkers, and finally protein markers were screened out; 6) Use the final screened protein markers to build a diagnostic model The protein markers screened in step 5) were screened using WPCNA, and the different modules generated after clustering were associated with the groups to find the module most relevant to the group. All the proteins in the module were annotated and enriched, and the top 20 proteins in the module were further selected by random forest importance sorting. Multiple ordered logistic regression models were constructed by random combination to predict the grouping variables. After five-fold cross-validation, the prediction model with the largest average AUC was found as the optimal prediction model.

9. Use of the molecular model for assisting the assessment of kidney damage in diabetic nephropathy established based on proteomics detection of serum samples as described in any one of claims 1 to 8 in the preparation of a kit for assisting the assessment of kidney damage in diabetic nephropathy.

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